In a nutshell: we have our base html skeleton ( layout.html) and our dashboard.html which extends the base. The template files should look familiar to you if you have worked with Flask/Jinja before.
#Bokeh python install#
To simulate our exisiting application, we quickly install Flask into a virtual environment, and create a simple app.py.
Nevertheless want to refresh your visualizations in short intervals using AJAX requests. Have an existing Flask app that should do the processing / data delivery. Don’t want to run another server component (i.e. If you want your visualizations to be based on large datasets, use streaming data, auto-downsamling for efficiency, and other goodness, you can use the Bokeh Server component. The plots are then drawn and made interactive using BokehJS, the client-side JavaScript library, that needs to be included in your page. The stand-alone visualizations are generated once in your Python environment and then saved to a file (or just delivered to a browser). With Bokeh you can build stand-alone and server-based data visualizations. If you plan on doing something similar, the following write-up hopefully saves you some time and pitfalls. This meant fiddling around a bit and reading more documentation. The samples looked nice, so I played around a bit, mostly following the accessible Quick Start guide.Įventually, I decided to build a small dashboard with Bokeh for an existing Flask application and wanted the plots to automatically fetch data updates using AJAX requests. from otting import figure, output_file, show,save, ColumnDataSource from import HoverTool from ansform import factor_cmap from bokeh.palettes import Blues8 from bokeh.embed import components import pandas as pd #Read csv file dataset= pd.read_csv("cars.csv") #Create ColumnDataSource from data frame source = ColumnDataSource(dataset) output_file("index.html") #Car List car_list = () #Add plot p= figure(y_range=car_list, plot_width=800, plot_height=600, title="Car with Top HP", x_axis_label="Horsepower", tools="pan,box_select,zoom_in,zoom_out,save,reset") #Render glyph/ For cmap use fill_color instead of color p.hbar(y="Car", right="Horsepower",left=0,height=0.4,fill_color=factor_cmap("Car", palette=Blues8, factors=car_list), fill_alpha=0.9, source=source, legend="Car" ) #Add Legend p.legend.orientation="vertical" p.legend.location="top_right" p.legend.label_text_font_size="10px" #Ad Tooltips: hover =HoverTool() hover.tooltips =""" Price: HP: """ p.During the weekend, I discovered Bokeh, a Python visualization library for the web. The last thing is how it is possible to generate a div and a script tag if we want to put this into another file. from otting import figure, output_file, show,save, ColumnDataSource from import HoverTool import pandas as pd #Read csv file dataset= pd.read_csv("cars.csv") #Create ColumnDataSource from data frame source = ColumnDataSource(dataset) output_file("index.html") #Car List car_list = () #Add plot p= figure(y_range=car_list, plot_width=800, plot_height=600, title="Car with Top HP", x_axis_label="Horsepower", tools="pan,box_select,zoom_in,zoom_out,save,reset") #Render glyph p.hbar(y="Car", right="Horsepower",left=0,height=0.4,color="orange",fill_alpha=0.5, source=source ) #Ad Tooltips: hover =HoverTool() hover.tooltips =""" Price: HP: """ p.add_tools(hover) #Show results show(p) #Save file save(p)Ĭolor Frames/Color Mapper: Let’s create a color map with legend from otting import figure, output_file, show,save, ColumnDataSource from import HoverTool from ansform import factor_cmap from bokeh.palettes import Blues8 import pandas as pd #Read csv file dataset= pd.read_csv("cars.csv") #Create ColumnDataSource from data frame source = ColumnDataSource(dataset) output_file("index.html") #Car List car_list = () #Add plot p= figure(y_range=car_list, plot_width=800, plot_height=600, title="Car with Top HP", x_axis_label="Horsepower", tools="pan,box_select,zoom_in,zoom_out,save,reset") #Render glyph/ For cmap use fill_color instead of color p.hbar(y="Car", right="Horsepower",left=0,height=0.4,fill_color=factor_cmap("Car", palette=Blues8, factors=car_list), fill_alpha=0.9, source=source, legend="Car" ) #Add Legend p.legend.orientation="vertical" p.legend.location="top_right" p.legend.label_text_font_size="10px" #Ad Tooltips: hover =HoverTool() hover.tooltips =""" Price: HP: """ p.add_tools(hover) #Show results show(p) #Save file save(p) Here is how we create a more interactive graph by using Bokeh. 
Up to now, we return the car vs horsepower chart but we also have image and price columns. The link below contains documentation related to different types of bar charts with Bokeh:
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